diff --git a/codes/models/diffusion/gaussian_diffusion.py b/codes/models/diffusion/gaussian_diffusion.py
index 6689922e..ae145143 100644
--- a/codes/models/diffusion/gaussian_diffusion.py
+++ b/codes/models/diffusion/gaussian_diffusion.py
@@ -12,6 +12,7 @@ import random
 import numpy as np
 import torch
 import torch as th
+from torch.distributions import Normal
 from tqdm import tqdm
 
 from models.diffusion.nn import mean_flat
@@ -366,14 +367,17 @@ class GaussianDiffusion:
             return x
 
         if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+            assert 'why are you doing this?'
             pred_xstart = process_xstart(
                 self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
             )
             model_mean = model_output
         elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
             if self.model_mean_type == ModelMeanType.START_X:
+                assert 'bad boy.'
                 pred_xstart = process_xstart(model_output)
             else:
+                eps = model_output
                 pred_xstart = process_xstart(
                     self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
                 )
@@ -391,6 +395,7 @@ class GaussianDiffusion:
             "variance": model_variance,
             "log_variance": model_log_variance,
             "pred_xstart": pred_xstart,
+            "pred_eps": eps,
         }
 
     def _predict_xstart_from_eps(self, x_t, t, eps):
@@ -510,7 +515,7 @@ class GaussianDiffusion:
                 cond_fn, out, x, t, model_kwargs=model_kwargs
             )
         sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"], "mean": out["mean"], "log_variance": out["log_variance"]}
+        return {"sample": sample, "pred_xstart": out["pred_xstart"], "pred_eps": out["pred_eps"], "mean": out["mean"], "log_variance": out["log_variance"]}
 
     def p_sample_loop(
         self,
@@ -673,7 +678,8 @@ class GaussianDiffusion:
         indices = list(range(self.num_timesteps))[::-1]
 
         img = noise
-        logperp = 1
+        #perp = self.num_timesteps
+        logperp = 0
         for i in tqdm(indices):
             t = th.tensor([i] * shape[0], device=device)
             with th.no_grad():
@@ -686,20 +692,16 @@ class GaussianDiffusion:
                     cond_fn=cond_fn,
                     model_kwargs=model_kwargs,
                 )
-                mean = out["mean"]
-                var = out["log_variance"].exp()
-                q = self.q_sample(truth, t, noise=noise)
-                err = out["sample"] - q
-                def normpdf(x, mean, var):
-                    denom = (2 * math.pi * var)**.5
-                    num = torch.exp(-(x-mean)**2/(2*var))
-                    return num / denom
+                eps = out["pred_eps"]
+                err = noise - eps
 
-                logperp = torch.log(normpdf(err, mean, var)) / self.num_timesteps + logperp
-        # Remove -infs, which do happen pretty regularly (and penalize them proportionately).
-        logperp[torch.isinf(logperp)] = torch.max(logperp) * 2
-        print(f'Num infs: : {torch.isinf(logperp).sum()}')  # probably should just log this.
-        return -logperp.mean()
+                m = Normal(torch.tensor([0.0]), torch.tensor([1.0]))
+                nprobs = m.cdf(-err.abs().cpu()) * 2
+                logperp = torch.log(nprobs) / self.num_timesteps + logperp
+                #perp = nprobs * perp
+        print(f'Num infs: : {torch.isinf(logperp).sum()}')  # probably should just log this separately.
+        logperp[torch.isinf(logperp)] = logperp.max() * 2
+        return -logperp
 
     def ddim_sample(
         self,